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1 | initial version |

Some time ago I've implemented 5-pt algorithm (according to Nister solution: see *An Efﬁcient Solution to the Five-Point Relative Pose Problem*).
The code uses additional packages: Eigen (for singular value decomposition) and rpoly.cpp (Jenkins-Traub real roots finder) and had about 500 lines of code (part of the code was generated in Matlab).

Experiments with synthetic data showed that it's more accurace and less sensitive to noise than 7-pt or 8-algorithms.

If you are interested I can share a source code so it can be adapted and added to OpenCV.

2 | No.2 Revision |

Some time ago I've implemented 5-pt algorithm (according to Nister solution: see *An Efﬁcient Solution to the Five-Point Relative Pose Problem*).
The code uses additional packages: Eigen (for singular value decomposition) and rpoly.cpp (Jenkins-Traub real roots finder) and had about 500 lines of code (part of the code was generated in Matlab).

Experiments with synthetic data showed that it's more accurace and less sensitive to noise than 7-pt or ~~8-algorithms. ~~8-pt algorithms. And, as theory says, doesn't suffer from planar degeneracy.

If you are interested I can share a source code so it can be adapted and added to OpenCV.

3 | No.3 Revision |

Some time ago I've implemented 5-pt algorithm (according to Nister solution: see *An Efﬁcient Solution to the Five-Point Relative Pose Problem*).
The code uses additional packages: Eigen (for singular value decomposition) and rpoly.cpp (Jenkins-Traub real roots finder) and ~~had ~~has about 500 lines of code (part of the code was generated in Matlab).

Experiments with synthetic data showed that it's more accurace and less sensitive to noise than 7-pt or 8-pt algorithms. And, as theory says, doesn't suffer from planar degeneracy.

If you are interested I can share a source code so it can be adapted and added to OpenCV.

4 | No.4 Revision |

Some time ago I've implemented 5-pt algorithm (according to Nister solution: see *An Efﬁcient Solution to the Five-Point Relative Pose Problem*).
The code uses additional packages: Eigen (for singular value decomposition) and rpoly.cpp (Jenkins-Traub real roots finder) and has about 500 lines of code (part of the code was generated in Matlab).

Experiments with synthetic data showed that it's more accurace and less sensitive to noise than 7-pt or 8-pt algorithms. And, as theory says, doesn't suffer from planar degeneracy.

If you are interested I can share a source code so it can be ~~adapted ~~modified and added to OpenCV.

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